A TPMS is an electronic system that monitors the air pressure inside a tire on a vehicle. TPMS report real-time tire-pressure information to the driver of the vehicle, either via a gauge, a pictogram display, a low-pressure warning light, or other technique. A TPMS with an auto localization feature is able to locate the exact tire which transmits the data from a tire pressure sensor (TPS) module located in a tire to an electronic control unit (ECU). Conventional systems for auto-localization correlate the phase information of an anti-lock braking system (ABS) wheel speed sensor with phase information received from a TPS module on a system level. A conventional TPS module generates the phase information using information from an acceleration sensor that detects accelaration of the tire in which the TPS is installed.
One approach to auto-localization includes Angular Position Sensing (APS). APS enables a TPMS to calculate the wheel speed and angular position of the wheel in which a TPS is installed based on a set of measured acceleration measurements received from the TPS. The calculated angular position is then correlated with data derived from the vehicle's ABS sensors.
Conventional approaches to APS involve an APS function that is based on measurements from a motion sensor or acceleration sensor. The accuracy of conventional APS functions is dependent on the signal to noise ratio (SNR) between unwanted noisy acceleration signals and desired acceleration signals. Unwanted noisy acceleration signals may be produced by mechanical vibration of the tire or the wheel. The desired acceleration signal in a conventional, acceleration-based system is induced by gravity, and may be within a range of (−1 g, +1 g). Thus, the accuracy of conventional APS is affected by the roughness of the road or other surface the vehicle is driving on, as well as mechanical vibrations from the tire itself. Rough roads may lead to unreliable APS results due to poor SNR, which in turn may result in unusable auto-localization and repeated APS measurements, requiring increased usage of computational resources and energy. Conventional approaches to APS may also require time consuming and costly exhaustive testing of different road conditions. Some conventional approaches oversample the acceleration sensor to overcome poor SNR, but oversampling the acceleration sensor also consumes more energy and computational resources. Furthermore, if a desired SNR cannot be achieved with oversampling, a conventional approach may try to gather new acceleration information, and the calculations may be repeated. Filtering outliers also increases the number of APS trials required by conventional approaches, further increasing the charge consumption of the TPS and also increasing the time required until a localization is successfully calculated.
Conventional approaches to auto-localization using APS are limited to approximately 180 km/h. Centrifugal force in the radial direction of the wheel being measured may cause z-axis acceleration to vary from 0 g to over 1600 g. A z-axis acceleration of 1600 g is associated with a tire speed of 300 km/h. TPMS sensors are expected to operate in conditions in which they have to manage large dynamic acceleration ranges, but conventional TPMS sensors are limited to a maximum acceleration of 500 g, or 180 km/h. Conventional approaches also suffer from a limited resolution of acceleration. Thus, conventional APS have degraded accuracy because the range of desired acceleration signals used for APS is (−1 g˜+1 g) while the direct current (DC) offset may reach up to 1600 g. Furthermore, during acceleration or braking of the vehicle, the centrifugal force in the radial direction of the wheel is not constant in time. More complex algorithms may therefore be used by conventional approaches, including DC-offset subtraction and robust optimization, to avoid unreliable APS results. Such complex algorithms result in conventional approaches that use additional computational resources and energy. For example, costly 10-13 bit analog to digital conversion (ADC) implementations with low-noise are required to provide the necessary dynamic range and sufficiently high resolution, while DC offset may be dependent on vehicle speed and may require more complex software, more computation time, and more energy. Conventional approaches thus may lead to increased use of computational resources, increased energy consumption, increased battery size, increased weight, and higher costs.